Business metrics do a great job summarizing the past. But if you want to predict how customers will respond in the future, there is one place to turn — predictive analytics. By learning from your abundant historical data, predictive analytics provides something beyond standard business reports and sales forecasts: actionable predictions for each customer. These predictions encompass all channels, both online and off, foreseeing which customers will buy, click, respond, convert or cancel. If you predict it, you own it.

The customer predictions generated by predictive analytics deliver more relevant content to each customer, improving response rates, click rates, buying behavior, retention and overall profit.
For online applications such as e-marketing and customer care
recommendations, predictive analytics acts in real-time, dynamically
selecting the ad, web content or cross-sell product each visitor is
most likely to click on or respond to, according to that visitor's
profile.
This is AB selection, rather than just AB testing.

Predictive Analytics for Business, Marketing and Web is a concentrated training program instructed by the founding chair of Predictive Analytics World that includes interactive breakout sessions and a brief hands-on exercise. In two days we cover:

The techniques, tips and pointers you need in order to run a successful predictive analytics and data mining initiative

How to strategically position and tactically deploy predictive analytics and data mining at your company

How to bridge the prevalent gap between technical understanding and practical use

How a predictive model works, how it's created and how much revenue it generates

Several detailed case studies that demonstrate predictive analytics in action and make the concepts concrete

NEW TOPIC: Five Ways to Lower Costs with Predictive Analytics

No background in statistics or modeling is required. The only specific knowledge assumed for this training program is moderate experience with Microsoft Excel or equivalent.

In order to meet the unique training needs of business decision makers and analytics practitioners, this training program is:

Business-focused. Unlike other training programs that also cover scientific, engineering and medical applications of data mining and analytics, this seminar focuses squarely on solving business and marketing problems with these methods.

Comprehensive across business needs. Within this realm, however, we step beyond the standard application of response modeling for direct marketing to solve the wider range of business problems listed below.

Vendor-neutral and method-neutral. This training program, which is not run by an analytics software vendor, provides a balanced view across analytics tools and methods.

In other words, customer prediction drives business actions, which deliver business results. We cover case studies across this range of applications, with detailed examples running through both days of the training program.

Creating predictive models

Data is your most valuable asset. It represents the entire history of your organization and its interactions with customers. Predictive analytics taps this rich vein of experience, mining it to produce predictive models. Where multi-channel data is available, predictive analytics discovers interactions across customer touch points, such as key online behavior that may predict which customers will respond to direct mail.

Whatever the application, the core methodology of predictive modeling is the same. We will uncover, in concrete terms, how modeling transforms your data into actionable customer predictions. To this end, we will see exactly what a model is, taking a look inside to see how it works and how it is created. Then we will:

Explore several example models in action

Turn the knobs that tweak and control modeling

Compare and contrast modeling methods intuitively, visualizing their differences so it all makes sense:

Decision trees

Business rules

Naive Bayes

Linear regression

Logistic regression

Neural networks

Other more recent advanced modeling techniques

Live demo of predictive analytics software. Witnessing analytics software in action makes the ideas, concepts and methods covered by this training program concrete. The training agenda includes a detailed demonstration of CART (Salford Systems), a tool specialized for decision trees. Its friendly GUI-based capabilities make the predictive model transparent so we can drill down and really see the inner workings of specific examples. A second broader-use analytics tool is also demonstrated via a short pre-recorded video with the instructor.

In addition to the products demonstrated, we will discuss the full spectrum of today's predictive analytics software, including free tools, cheap tools, and complete software suites.

Measuring how well predictive models work

Once you've got a predictive model, how do you know how good it is? We cover methods to evaluate models, which fall into two groups:

Forecasting: How large a boost in revenue, sales or profit will the model produce?

Accuracy: How well does it predict, how often is it correct, and how much better is it than standard segmentation such as RFM?

Deploying a predictive model is playing a numbers game that puts the odds in your favor and improves the effectiveness of campaigns, operations and web behavior. We create profit curves, ROI calculations and bottom-line analyses and talk through exactly what they're telling us. And we prepare for performance gotchas that sneak up on you.

Management and project leadership for predictive analytics

Although predictive analytics is technical at its core, it must be run as a business activity in order to generate customer predictions that have a business impact. This requires a wholly collaborative process driven by business needs and marketing expertise. This ensures that customer predictions are actionable within your company's operational framework, and that they have the greatest impact within your company's business model.

Referencing the industry standard data mining process model (called CRISP-DM), we break down the requirements of a predictive analytics business initiative. We explore this process, by which analysts and managers collaborate to strategically position predictive analytics, sustain universal buy-in and understanding, and avoid common roadblocks and unforeseen hazards.

Like sky-diving and SCUBA diving, after a few hours of learning predictive analytics, it's a good time to dive right in. To this end, the training program includes breakout sessions, which are integrated with the conceptual flow of topics covered. You will join a small team and actively collaborate to design deployment strategies for predictive analytics. Working together to solve specific business problems, you will design strategic processes that avert organizational challenges, and you will design a broad technical approach, including the data discovery, data preparation and evaluatory metrics needed to direct a predictive analytics initiative.

These engaging breakout sessions are conducive to exercising the concepts you've learned, making them more intuitive and ingrained, and also provide an opportunity to learn from colleagues.

You will also "get your hands dirty" by digging through some data
with a hands-on exercise during the second day. Optionally working
with a buddy for this short exercise of about 20 minutes, you will
bring a predictive model to life and see it improve before your eyes.

The following short, published articles, written by the instructor, are a great place to get started. Note that these articles are not required reading; the material therein will be covered during the training program.

Seven Reasons You Need Predictive Analytics Today
Predictive analytics has come of age as a core enterprise practice necessary to sustain competitive advantage. This definitive white paper reveals seven strategic objectives that can be attained to their full potential only by employing predictive analytics, namely Compete, Grow, Enforce, Improve, Satisfy, Learn, and Act.

Eric Siegel, Ph.D., is a seasoned consultant in data mining
and analytics, author of the notable book, Predictive
Analytics: The Power to Predict Who Will Click, Buy, Lie, or
Die, an acclaimed industry instructor, and an award-winning teacher of graduate-level courses in
these areas. Eric served as a computer science professor at Columbia
University, where he developed data mining technology in the
realms of machine learning performance optimization, integrating
historical databases, text mining, and data visualization. The
founding conference chair of Predictive Analytics World and Text
Analytics World, Eric has authored 11 peer-reviewed research
publications and ran an MIT-hosted symposium on data mining. He also
co-founded two New York City-based software companies for
customer/user profiling and data mining. With data mining, Eric has
solved problems in CRM analytics, computer security, fraud detection,
text mining and information retrieval.

Eric has taught industry programs through Prediction Impact, The Modeling Agency and Salford Systems. In addition, he taught many semesters of university courses, including data mining-related graduate courses as well as introductory lecture series for non-technical audiences. Two of these courses have been in syndication through the Columbia University Video Network. Eric also published three peer-reviewed papers on computer science education.

"The best training seminar I have ever attended. I loved how the seminar was geared around theory and application rather than learning about an individual statistical mining tool."

Ryan Williams
Mgr Customer Analytics
GSI Commerce

"An excellent overview on how to start using predictive analytics in
any organization! In just two weeks I already have buy-in from upper
management to explore ways to use predictive analytics to improve
up-sells, cross sells and to determine what lifestyle imagery to
display to our users. I'm super excited about these projects."

Jennifer Boland
Onsite Marketing Analyst
Sierra Trading Post

"At Intuit we're already using data as an asset on the web, but this course makes it very concrete how we can take it to the next level."

Jared Waxman
Web Analytics Leader
Intuit

"The best part of this training program is the clear correlation to practical applications in everyday business."

Reto Matter
VP Business Intelligence
PlanetOut Inc.

"Eric is an A+++ instructor with a great sense of humor."

Ali Maleki
Project Manager
Computer Tech. Consultants

"A very insightful and interesting seminar. I plan to put data mining
and predictive analytics to work for us right away thanks to your
ability to make this an approachable subject."

Rob Ford
Director Pricing
Getty Images

"Making predictive analytics clear, simple and even entertaining is a tall order, but this seminar does just that! There's a mammoth load of material here, presented in understandable, actionable terms."

"Eric reveals a roadmap to the use of predictive modeling in achieving
business objectives, emphasizing the alignment of the resources
critical to success. He balances a refreshing confidence in academic
models with a practical regard for the inevitable fallibility of
real-world data, and leads the way with a contagious sense of
exploration and discovery. I learned a great deal in two days and had
fun doing so."